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Import name tinymfv -> moralmaps, pip name tiny-mfv -> moral-maps, GitHub URLs wassname/tinymfv -> wassname/moral-maps. HuggingFace dataset id wassname/tiny-mfv left as-is (separate namespace, published data artifact). Historical docs/spec/* and RESEARCH_JOURNAL keep their dated paths. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
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2.3 KiB
AGENTS.md
This is fail-fast research code. Novel work, not in your training data. Extrapolate carefully.
What moralmaps is
One answer-token logprob reader that runs many questionnaires. The model is prefilled to an answer slot after a short think budget; we read the next-token distribution over the vocab and gather the instrument's answer tokens. Two instrument kinds share that reader:
- nominal (MFV vignettes): the answer IS the foundation; profile = choice frequency; steer effect
is a multicategory logit change
delta score[f](full-vocab logprob, nats). - ordinal (MFQ-2, Big5, 16PF, humor): the answer is a scale point 1..M; reported as a renormalized categorical. The human-comparable summary is the expected score E; the steering-legible summary is the agree-vs-disagree log-odds (E is a bounded mean and saturates, see docs/reviews/).
The load-bearing primitive is lp_gather: the full-vocab logprobs at the answer tokens, per
(item, frame, sample). Keep it. Every readout (E, log-odds, entropy, profile) is a pure function
of it. Do not throw it away by renormalizing early.
Conventions
- Fail fast. No defensive programming, no silent fallbacks, no backward-compat shims. A
config['key']KeyError beats aconfig.get('key', 0)that fabricates a valid-looking number. - NaN-at-collapse is intentional. When
pmass(mass on allowed tokens) collapses, the renormalized distribution reads NaN by design: "do not compare." Do not guard it with an eps/softmax fallback. - No accretion. If you add something, remove something of equal weight. Delete unused code rather than marking it legacy.
- ASCII only in code and prose, with a carve-out for math symbols (greek vars,
>=, arrows in tables,delta). No em-dashes for parentheticals; use commas. - Edit files with edit tools (reviewable diffs), not cat/sed/echo.
- The correctness gate is a fast smoke run on a tiny random model (real LLM, real eval, tiny scale),
not a
tests/dir. If a bug slips past, strengthen the smoke path.
Code style
- einops/einsum for shape ops and contractions; jaxtyping on function boundaries only.
- loguru for logging; polars v1 API for dataframes (eval.py still uses pandas, fine).
- Single-letter dims (b, s, h, d); capital suffix for projected spaces (hsS after
@ U). - Comments explain why, not what. Long method-history belongs in docs/, not inline.